Overview
On Site
140k - 200k
Full Time
Skills
Management
Recruiting
Analytics
Artificial Intelligence
Aerospace
Manufacturing
Data Science
Operational Excellence
CaliberRM
Workflow
Training
Real-time
Kubernetes
Terraform
Research
System Integration Testing
Financial Software
Machine Learning (ML)
Financial Services
Python
Pandas
NumPy
PyTorch
Machine Learning Operations (ML Ops)
Google Cloud Platform
Google Cloud
Continuous Integration
Continuous Delivery
Data Processing
Collaboration
SAP BASIS
Job Details
A leading growth and venture investment firm with over $25B under management is hiring a Software Engineer, Machine Learning Operations (MLOps) to help shape the next generation of its proprietary data and analytics platform. This firm invests in category-defining companies - from AI and aerospace to advanced manufacturing and health - and differentiates itself by building technology internally that fuels smarter, faster investment decisions. Their engineering and data science teams work side-by-side with investment leaders to transform how insight and operational excellence are created.
In this role, you'll be part of a small, high-caliber team developing production-grade ML systems that power critical investment workflows. You'll design and deploy scalable infrastructure for model training, deployment, and monitoring - integrating directly with real-time data pipelines and internal APIs. The environment is modern (Python, PyTorch, Kubernetes, Google Cloud Platform, Terraform, BigQuery) and collaborative, blending research experimentation with rigorous engineering. If you're the kind of engineer who thrives on ownership, loves building things that actually make an impact, and wants to sit at the intersection of machine learning, data, and real-world business decisions, this is that rare opportunity to do it at scale - inside one of the most forward-thinking firms in the industry.
Required Skills & Experience
Tech Breakdown
#DK-1
In this role, you'll be part of a small, high-caliber team developing production-grade ML systems that power critical investment workflows. You'll design and deploy scalable infrastructure for model training, deployment, and monitoring - integrating directly with real-time data pipelines and internal APIs. The environment is modern (Python, PyTorch, Kubernetes, Google Cloud Platform, Terraform, BigQuery) and collaborative, blending research experimentation with rigorous engineering. If you're the kind of engineer who thrives on ownership, loves building things that actually make an impact, and wants to sit at the intersection of machine learning, data, and real-world business decisions, this is that rare opportunity to do it at scale - inside one of the most forward-thinking firms in the industry.
Required Skills & Experience
- BS in CS, ML or related field
- 5+ years of experience building high performance and/or financial systems
- Experience building production software in Python
- 2+ years of building Machine Learning infrastructure
- Experience with MLOps tooling like MLFlow or DVC
- Strong communicator that thrives in a fast-paced environment
- Financial services experience is a plus
Tech Breakdown
- Python (Pandas, NumPy, PyTorch)
- MLOps
- Google Cloud Platform Infrastructure
- CI/CD
- Data Processing
- 80% Hands-on
- 20% Team Collaboration
- Competitive Bonus
- Comprehensive Benefits
- Flexible PTO
- 3 Days in West Loop office, 2 days WFH
#DK-1
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.